Computational Method Streamlines Spectral Imaging, Cuts Costs

 

The versatility and precision of hyperspectral imaging make it an indispensable tool in numerous scientific and industrial applications, from medical imaging to environmental monitoring to quality control. But traditional hyperspectral imaging systems can be costly, cumbersome, and challenging to scale.

A computational spectral imaging system from the University of Utah provides a fast, inexpensive, efficient alternative to capturing high-quality spectral data. The system, which the team tested across biomedical, food-quality, and astronomical use cases, could establish a new framework for high-speed, high-fidelity spectral imaging with broad translational potential.

The system uses a diffractive filter array to project spectral information into the spatial domain, enabling the capture of a single-channel, 2D image that contains both spatial and spectral data. This 2D image, called a diffractogram, is computationally decoded to reconstruct a spectral image cube with 25 spectral bands in the 440-800 nm range. Each of the 25 separate images that comprise the cube represents a distinct slice of the visible spectrum.

The team modeled and designed the diffractive filter array and the algorithm to reconstruct the hyperspectral images from the raw data captured by the sensor.

By encoding a scene into a single, compact 2D image rather than a massive 3D data cube, the camera makes hyperspectral imaging faster and more efficient. The fast encoding enables the system, which is small enough to fit into a cellphone, to take high-speed, high-definition video.

“One of the primary advantages of our camera is its ability to capture the spatial-spectral information in a highly compressed, two-dimensional image instead of a three-dimensional data cube, and use sophisticated computer algorithms to extract the full data cube at a later point,” professor Apratim Majumder said. “This allows for fast, highly compressed data capture.”

The current prototype camera can take images at just over one megapixel in size (1304 x 744 pixels) and break them down into 25 separate wavelengths across the spectrum. The diffractive element, placed directly over the camera’s sensor, encodes spatial and spectral information for each pixel on the sensor.

“We introduce a compact camera that captures both color and fine spectral details in a single snapshot, producing a ‘spectral fingerprint’ for every pixel,” professor Rajesh Menon said.

To demonstrate the camera’s capabilities, the researchers applied standard inferencing techniques to reconstructed spectral images across various sectors. The system demonstrated a spectral reproduction error of less than 15% across the 440-800 nm band.

The researchers used the camera to classify lung and trachea tissues in ex vivo chicken lung images, predict the freshness of strawberries, and mimic the spectral filters that are used in stellar imaging. These experiments highlighted the system’s potential in medical diagnostics, food-quality assessment, and astronomical observations.

The diffractive, computational spectral imaging system offers several advantages. It provides snapshot capability, eliminating issues with scan-and-stitch methods. The diffractogram serves as a form of optical compression, efficiently storing spatio-spectral content in a compact, information-rich 2D array. This is particularly beneficial for applications with limited storage or transmission bandwidth, such as airborne or satellite imaging.

“Satellites would have trouble beaming down full image cubes, but since we extract the cubes in post-processing, the original files are much smaller,” Majumder said.

The system also provides the flexibility to perform reconstructions offline and on-demand, after data capture, for scenarios with limited on-board computational resources. Also, since the diffractogram encodes spectral information continuously, it allows for information to be selected on an application-specific spectral basis, yielding smaller image cubes and faster, more stable reconstructions.

Compared to traditional hyperspectral imaging systems, the computational spectral imager’s streamlined approach reduces costs significantly.

“Our camera costs many times less, is very compact and captures data much faster than most available commercial hyperspectral cameras,” Majumder said. “We have also shown the ability to post-process the data as per the need of the application and implement different classifiers suited to different fields such as agriculture, astronomy, and bioimaging.”

Hyperspectral cameras have long been used in agriculture, astronomy, and medicine, where subtle differences in color can make a big difference. But these cameras have historically been bulky, expensive, and limited to producing still images.

“When we started out on this research, our intention was to demonstrate a compact, fast, megapixel-resolution hyperspectral camera, able to record highly compressed spatial-spectral information from scenes at video-rates, which did not exist,” Majumder said.

“This work demonstrates a first snapshot, megapixel, hyperspectral camera,” he said. “Next, we are developing a more improved version of the camera that will allow us to capture images at a larger image size and an increased number of wavelength channels, while also making the nanostructured diffractive element much simpler in design.”

By making hyperspectral imaging cheaper, faster, and more compact, the computational camera advances spectral imaging technology and potentially opens the way for technologies that could change the way the world and its contents are seen.

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Handheld Sensor Detects Markers for Early-Stage Alzheimer’s

        

A newly-developed, handheld optical sensor could make Alzheimer’s disease easier to detect in its early stages, when treatments for the disease are most effective. The photonic resonant sensor is the result of a collaboration among researchers at the University of York, the University of Strathclyde, and the University of São Paulo.

The team developed a sensor that can simultaneously detect two of the amyloid peptides that are indicators for Alzheimer’s, at the levels clinically required for diagnosis. The capability to simultaneously detect beta amyloid 40 and beta amyloid 42 in the blood opens a route to quantifying and analyzing their ratio, enabling the progression of the disease to be tracked. Single biomarker detection is insufficient for clinical diagnosis.

Photonic resonant sensors allow for the label-free detection of specific molecules, in addition to surface imaging and the multiplexing of different biomarkers. They are compatible with low-cost fabrication processes and can be implemented with minimal optoelectronic elements for the signal readout.

Detecting peptides, however, remains a challenge for this class of sensors, mainly due to the low molecular weight of the peptides. Amyloid peptides are small and occur at low concentrations.

To ensure a high-performing sensor that could detect peptides in the blood, the researchers integrated gold nanoparticles with a dielectric nanopillar photonic crystal structure in a dimer configuration. The gold nanoparticles amplified the optical signal used to detect Alzheimer’s disease biomarkers dramatically, compared to the team’s previous sensor design, which involved the use of parallel grooves.

“This new design has allowed us to detect the amyloid biomarkers at the ultralow, clinically relevant concentrations we need, which our previous sensor couldn’t quite reach,” researcher Steven Quinn said. “The added bonus is that the technology remains scalable, mass-producible, and we aim for it to be as simple to use as a Covid test.”

The sensor design combines high resonance Q-factor, amplitude, and sensitivity, leading to a high figure of merit. “When you compare different technologies in photonics, you use a ‘figure of merit,’ which is like a scorecard that takes into account key parameters like sensitivity and signal-to-noise ratio,” Quinn said. “Our new sensor’s scorecard outperforms competing technologies.”

The sensor can detect beta amyloid 40 and beta amyloid 42 peptides in the same channel, which is relevant for assessing disease progress, and opens a route toward multiplexing. To achieve high selectivity and specificity in the sensor, the researchers used an immunoassay design approach.

The researchers are integrating the sensor technology into a handheld device. Potentially, this device could provide an indication of disease within seconds from a simple finger-prick of blood, at a projected cost of less than £100 per test. Low-cost, point-of-care testing could broaden accessibility to early Alzheimer’s testing and diagnosis, giving more patients access to treatments that are most effective in the initial stages of the disease.

“New Alzheimer’s treatments work by specifically targeting the sticky amyloid proteins that build up in the brain,” Quinn said. “For these drugs to be effective, doctors first need to confirm that a patient has this protein build-up — a condition known as amyloid positivity. A simple, scalable blood test could be the way to facilitate widespread access to these emerging treatments.”

Current methods for diagnosing Alzheimer’s disease, such as brain scans (PET/MRI) or invasive lumbar punctures, are costly, time-consuming, and are not readily accessible. Highly accurate, lab-based blood tests are now available, but they rely on large, expensive machinery, with a single test potentially costing thousands of pounds.

The next milestone for the team will be to validate the photonic sensor using blood samples from patients with Alzheimer’s and a healthy control group. This crucial phase will determine how effectively the sensor can differentiate between the two groups.

The new sensor technology could be used to detect other biomarkers and markers for other diseases. “The same principles and protocols can be used to detect a protein called phosphorylated tau, another key Alzheimer’s biomarker, as well as alpha-synuclein in Parkinson’s disease,” Quinn said. “We believe this could become a platform technology to help differentiate between various forms of dementia, which is a major challenge for clinicians.”

Although the team still needs to demonstrate the effectiveness of the sensor in patient samples, it believes that the photonic biosensor holds significant promise as a cost-effective tool to open the door to widely available testing for Alzheimer’s and other neurodegenerative diseases.

“Our vision is a device that is user-friendly for clinicians and can be deployed in healthcare settings around the world,” Quinn said.

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Fiber Photometry Hastens Development of Alzheimer’s Disease Therapies

Use of mouse models to test new interventions for Alzheimer’s disease is a cornerstone of Alzheimer’s disease therapeutic development.

Current preclinical evaluation of Alzheimer’s disease pathology relies mostly on post-mortem analyses of animal models, which limits researchers’ ability to follow the progression of the disease or the efficacy of treatments over time.

In search of a method to observe the development of the disease and its response to therapies in real-time, researchers at the University of Strathclyde and the Italian Institute of Technology (IIT) investigated fiber photometry, an optical approach to monitoring neural activity in live animals. The researchers expanded the capabilities of in vivo fiber photometry, using it to examine the pathological features of an AD mouse model in a freely behaving condition. This approach to could help researchers uncover information about how Alzheimer's disease develops and enable more flexible testing of potential therapies.

Using a conventional, flat fiber-based photometry approach, the team confirmed in its initial experiments that amyloid plaque signals could be monitored across multiple depths in in vivo Alzheimer's disease mice models under anesthesia.

Instead of relying on genetically encoded sensors, the researchers implemented a non-genetic strategy, and injected the mice with a blood-brain-barrier-permeable fluorescent tracer, Methoxy-X04. The hydrophobic structure of this compound allows it to enter the brain, where it specifically binds to beta-sheets found within amyloid fibrils, allowing visualization of amyloid plaques in Alzheimer's disease models.

The team found that the depth profiles of the in vivo fluorescent signals correlated with the plaque density measured afterward in brain slices. A machine learning model could distinguish between the in vivo fluorescent signals of mice with and mice without amyloid plaques based on the depth profiles of their signals.

The researchers then assessed whether tapered optical fibers would allow depth-resolved photometry for plaque signals in ex vivo tissue. Upon examination of brain tissue slices, they found that the tapered fibers reliably tracked plaque distribution.

After validating the tapered fiber-based photometry approach in freely behaving mice, implantation into chronically in living mice revealed depth-specific increases in fluorescence after Methoxy-X04 injection in Alzheimer's disease model mice, but not in healthy controls. The technique showed age-dependent signal increases consistent with disease progression.

By exploiting the photonic properties of tapered fibers, the researchers establish depth-resolved photometry of amyloid plaque signals in vivo and ex vivo.

In contrast to existing methods, such as optoacoustic tomography, the optical fiber-based approach allows long-term monitoring of amyloid pathology across multiple deep brain regions in freely behaving animals. While the photometry technique cannot resolve individual plaques, it can provide a minimally invasive way to track pathological changes across time and across brain regions.

Amyloid plaques have long been recognized as a hallmark of Alzheimer's disease. Recent therapeutics targeting amyloid-β protofibrils or deposited amyloid plaques have proven effective in patients, and researchers have successfully translated early preclinical mouse data to the clinic. As such, evaluating new interventions in preclinical mouse models should continue to play an important role in accelerating future treatments for Alzheimer's disease.

The results of this research demonstrate the potential of using fiber optic photometry, which has been widely used in the neuroscience community, to monitor plaque signals in order to optimize therapeutic approaches and develop intervention strategies for Alzheimer's in a preclinical setting.

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Hydrogel Improvements Boost Utility of Expansion Microscopy

Collaborators from Carnegie Mellon University, the University of Pittsburgh, and Brown University have described a microscopy technique and set of protocols that overcome a bottleneck to the expansion microscopy method. The collaborators developed “Magnify” as a variant of expansion microscopy that uses a hydrogel that retains a spectrum of biomolecules, offers a broader application to a variety of tissues, and increases the expansion up to 11× times linearly or approximately 1300 folds of the original volume.

Through the expansion microscopy process, samples are embedded in a swellable hydrogel that homogenously expands to increase the distance between molecules, which allows them to be observed in greater resolution. This allows nanoscale biological structures that previously could be viewed only via expensive high-resolution imaging techniques to be seen with standard microscopy tools.

In addition, the researchers said in their paper, “Current expansion microscopy protocols require prior treatment with reactive anchoring chemicals to link specific labels and biomolecule classes to the gel.”

In developing the Magnify method, according to the researchers, the team developed a gel that was mechanically sturdy to retain nucleic acids, proteins, and lipids without the need for a separate anchoring step.

Yongxin (Leon) Zhao, the Eberly Family Career Development Associate Professor of Biological Sciences at Carnegie Mellon, said, “We overcame some of the long-standing challenges of expansion microscopy. One of the main selling points for Magnify is the universal strategy to keep the tissue’s biomolecules, including proteins, nucleic acids, and carbohydrates, within the expanded sample.”

Keeping different biological components intact matters, since previous protocols required eliminating many various biomolecules that held tissues together, Zhao said. However, these molecules could contain valuable information for researchers.

“In the past, to make cells really expandable, you need to use enzymes to digest proteins, so in the end, you had an empty gel with labels that indicate the location of the protein of interest,” he said.

Using the Magnify method, the molecules are kept intact, and multiple types of biomolecules can be labeled in a single sample.

“Before it was like having single-choice questions. If you want to label proteins, that would be the version one protocol. If you want to label nuclei, then that would be a different version,” Zhao said. “If you wanted to do simultaneous imaging, it was difficult. Now with Magnify, you can pick multiple items to label, such as proteins, lipids, and carbohydrates, and image them together.”

Co-first author and postdoctoral researcher Aleksandra Klimas said that in addition to its high-resolution imaging properties, the newly described approach is advantageous because of its broad applicability. “Traditionally, you need expensive equipment and specific reagents and training. However, this method is broadly applicable to many types of sample preparations and can be viewed with standard microscopes that you would have in a biology laboratory,” she said.

Doctoral student Brendan Gallagher, an additional co-first author of the work, said that the team tried to make the protocols involved in its method as compatible as possible for researchers who could benefit from adopting Magnify. As a result, Gallagher said, Magnify works with different tissue types, fixation methods, and tissue that has been preserved and stored.

The developed protocols aim to provide a framework for those in neuroscience, pathology, and other biological and medical fields. According to the researchers, the small sizes of monomers, as well as the fast rate of diffusion, mean that Magnify may have applicability to thick tissues and whole organisms. “Magnify would be readily adaptable to generating nanoscale whole organ data sets, which currently rely on either lower-resolution tissue-clearing methods,” they said in their paper.

In addition, they said, because Magnify is a chemical strategy that does not rely on complex optics, its framework can be adapted to a range of imaging modalities and gel chemistries, as well as with other expansion microscopy strategies — which have previously demonstrated compatibility with existing superresolution techniques.


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Speckle-correlation Technique Recovers Images of Obscured Objects in Real Time

Imaging through a light-scattering medium, such as clouds in the sky or tissues in the body, poses special challenges. The scattered light must be reconstructed, typically by using complex optical elements in an environment that is vulnerable to motion and mechanical instability. Computational algorithms are then able to post-process the detected light to generate an image.

A new approach to imaging reconstruction, developed by researchers at King Abdullah University of Science and Technology (KAUST) and the Xiong’an Institute of Innovation, uses speckles to enable clear images of obscured objects, whether static or moving, to be produced in real time.

Previous strategies for reconstructing scattered light have required some knowledge of the object and the ability to control the wavefront of light illuminating it. These strategies have not used directly obtained random speckle patterns for imaging, due to degradation and scattering. Rather, speckles have been seen as noise or chaotic patterns.

Speckle-correlation imaging, which extracts information about the source from fluctuations in the intensity (i.e., speckles) in the transmitted light, could offer an efficient approach to reconstruction. However, many of the technologies based on speckle-correlation require time-consuming computational reconstruction. Also, some information, such as the image orientation and location, is missed.

The KAUST team investigated a way to directly observe self-imaging units of small objects by viewing speckles through diffusers. By carefully examining a speckle, the researchers found that they could obtain clear self-imaging phenomena from a single shot of the speckle image.

By examining the inherent nature of speckles, the researchers were able to develop a self-imaging speckle model and validate it in experiments. This facilitated a visual understanding of speckles and their properties.

The researchers obtained an image directly from a single shot of the speckle image by passing light from a small, standardized test object through a thin diffusing material. By moving the direction of the camera away from the diffuser, the researchers were able to build a 3D image by taking slices through the speckles. When the researchers viewed enlarged sections of these images, they could see reproductions of the test object.

This approach allowed the researchers to see directly through the random diffuser with the naked eye and use real-time video imaging. The orientation of the object could be directly seen and traced in real time.

The new method requires no complex, expensive equipment for the active control of light, and no prior knowledge of the source or diffusion medium. There is no need for iterations or parameter adjustments.

The researchers said that the visibility of directly observed imaging patterns using the new method is equivalent to those processed with direct speckle autocorrelation imaging (SAI). Furthermore, using a simply modified SAI method with efficient joint-filtering, the researchers achieved an imaging quality and resolution comparable to the best results processed by previously reported computational reconstruction methods, according to the team.

“We have developed a strategy of calibration-free, reconstruction-free, real-time imaging of static and moving objects with their actual orientation information,” researcher Wenhong Yang said. “This novel technique only requires simple or low-cost devices, without the post-computational reconstruction.”

The results provide a fresh perspective on diffuser-imaging systems and could inspire new rapid, high-quality imaging applications through scattering diffusers. Optical imaging through scattering light plays a crucial role in many industries, including biomedicine and astronomy.

“Our work presents a significant step in the field of scattering imaging and will shed light on new avenues for imaging through diffusive media,” Yang said.


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Spatial Light Modulation Gauges How Lenses Slow Progress of Myopia

Myopia, or nearsightedness, is one of the most common ocular disorders worldwide and a leading cause of visual impairment in children. Although specialized eyeglass lenses have been clinically tested to treat myopia progression, an in-depth optical characterization of the lenses has not yet been performed.

Researchers from the ZEISS Vision Science Lab at the University of Tübingen and the University of Murcia undertook a comprehensive characterization to investigate the properties of spectacle lenses designed to slow the progress of myopia. The results of their study could help increase the efficacy of future lens designs.

Myopia is typically caused when a person’s eyes become elongated, which affects how the eyes focus on faraway objects. The condition can progress in children and teens as their bodies grow.

To reproduce pupil shape and myopic ocular aberrations, researchers developed an instrument that reproduced the aberrations in myopic eyes and enabled physical simulation of the pupil. They based their instrument on spatial light modulation (SLM) technology.

“After exploring the state of the art, we didn’t find a method that could be used to characterize the optical properties of these eyeglass lenses under real viewing conditions,” said researcher Augusto Arias-Gallego. As a result, Arias-Gallego said, the researchers endeavored to build an instrument that can measure the lens’ optical response to different angles of illumination, while also reproducing the myopic eye’s pupil and refractive errors.

The team’s instrument uses an illumination source mounted on an arm that rotates around the lens. After the light passes through the lens, it is guided to an SLM by a rotating mirror. The SLM is composed of tiny liquid crystal cells that modify the propagating light, boosting its spatial resolution.

The SLM reproduces the refractive errors and pupil shape of myopic eyes, allowing the researchers to re-create myopic aberrations and to produce different aberrations depending on the angle of illumination. Using the SLM, the researchers programmed the aberrations as phase maps and induced programmed amounts of defocus to perform through-focus testing.

Tests helped the researchers determine the image quality within the proximity of a simulated retinal position, shedding light on how the special lens interacts with eye elongation signaled at the retina. “By combining the through-focus results with light-scattering measurements, we were able to accurately characterize several types of eyeglass lenses,” Arias-Gallego said. The researchers then compared measurements for each lens with their reported clinical efficacy for slowing myopia progression, he said.

The researchers quantified and compared the focusing and scattering properties of a single vision lens with two types of spectacle lenses for myopia progression management: defocus incorporated multiple segments (DIMS) and diffusion-optical technology (DOT). They calculated four optical metrics potentially related to myopia progress and quantified the scattered light from the peripheral lens zones. Scattering was quantified by implementing the optical integration method.

The characterization showed an increased contrast and sharpness of images through the DIMS lens at the peripheral retina when inducing myopic defocus, with respect to the single vision and DOT lenses. It further showed that contrast reduction by the DOT lens was dependent on the luminance at the pupil.

According to Arias-Gallego, the results both raised new questions and pointed to potential strategies that could increase the efficacy of future designs.

“Insights into the link between the optical properties of myopia progression management lenses and effectiveness in real-world scenarios will pave the way to more effective treatments,” Arias-Gallego said. “This could help millions of children and is fundamental in understanding the mechanisms by which these lenses work.”

The researchers are working to adapt the SLM instrument to include sources with varying wavelengths.

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TiHive Raises $9.3M to Advance Terahertz-AI Vision Technology

TiHive, a company focused on terahertz-AI vision systems, has raised €8 million ($9.3 million) to accelerate growth and expand internationally. The company’s technology combines industrial-grade, silicon-based terahertz imaging devices and AI to enable real-time, non-destructive, see-through quality and process control on production lines.

The company said the funding will support the commercialization of its industrial vision solutions, reinforce international deployment — particularly in hygiene, textiles, recycling, agriculture, and space industries — and accelerate R&D. The company aims to develop a new generation of terahertz chips with extended frequencies and advanced AI features.

TiHive’s systems are integrated directly on production lines and connected to the machines and to the cloud, measuring the quality and the process stability of thousands of products every minute. The technology platform uses CMOS technology, paired with advanced THz optics and an AI-powered software platform. By integrating terahertz technology on CMOS chips, TiHive’s approach enables miniaturization, scalable mass production, low energy consumption, and high-speed performance.

Founded in 2017 and currently employing 14 people, TiHive is backed by support from the EIC Accelerator and Bpifrance. Karista, a deep-tech hardware specialist, and Wind, a deep-tech venture capital fund, participated in the funding.

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